Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China

Source identification of heavy metals and metalloids in soil using open-source Tellus database and their impact on ecology and human health

The presence of heavy metals and metalloids (metal(loid)s) in the food chain is a global problem, and thus, metal(loid)s are considered to be Potentially Toxic Elements (PTEs). Arsenic (As), lead (Pb), mercury (Hg), and cadmium (Cd) are identified as prominent hazards related to human health risks throughout the food chain. This study aimed to carry out a source attribution for metal(loid)s in shallow topsoil of north-midlands, northwest, and border counties of the Republic of Ireland, followed by an assessment of the potential ecological and human health risks. The positive Matrix Factorization (PMF) was used for source characterization of PTEs, followed by the Monte Carlo simulation method, used for a probabilistic model to evaluate potential human health risks. The mean concentrations of prioritized metal(loid)s in the topsoil range in the order of Pb (28.83 mg kg-1) > As (7.81 mg kg-1) > Cd (0.51 mg kg-1) > Hg (0.11 mg kg-1) based on the open-source Tellus dataset. This research identified three primary sources of metal(loid) pollution: geogenic sources (36 %), mixed sources of historical mining and natural origin (33 %), and anthropogenic activities (31 %). The ecological risk assessment showed that Ireland's soil exhibits low-moderate pollution levels however, concerns remain for Cd and As levels. All metal(loid)s except Cd showed acceptable non-carcinogenic risk, while Cd and As accounted for high to moderate potential cancer risks. Potato consumption (if grown on land with elevated metal(loid) levels), Cd concentration in soil, and bioaccumulation factor of Cd in potatoes were the three most sensitive parameters. In conclusion, metal(loid)s in Ireland present low to moderate ecological and human health risks. It underscores the need for policies and remedial strategies to monitor metal(loid) levels in agricultural soil regularly and the production of crops with low bioaccumulation in regions with elevated metal(loid) levels.

Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China

Groundwater contaminated by potentially toxic elements has become an increasing global concern for human health. Therefore, it is crucial to identify the sources and health risks of potentially toxic elements, especially in arid areas. Despite the necessity, there is a notable research gap concerning the sources and risks of these elements within multi-layer aquifers in such regions. To address this gap, 54 phreatic and 24 confined groundwater samples were collected from an arid area in Northwest China. This study aimed to trace the sources and evaluate the human health risks of potentially toxic elements by natural background level (NBL), positive matrix factorization (PMF) model, and health risk model. Findings revealed exceeding levels of potentially toxic elements existed in phreatic and confined aquifers. Source apportionment and NBL results indicated that mineral dissolution, evaporation, redox reactions, and human activities were the main factors for elevated concentrations of potentially toxic elements. High Fe and Mn concentrations were attributed to reduction environments, while F accumulation resulted from slow runoff, and irrigation from the Yellow River. Due to high F levels, more than one-third of groundwater samples (phreatic: 33.14 %, confined: 56.22 %) posed non-carcinogenic health risks to population groups. Adults displayed higher carcinogenic risks (phreatic: 19.47 %, confined: 34.16 %) than infants (phreatic: 0 %, confined: 0 %) and children (phreatic: 1.26 %, confined: 7.97 %) owing to the toxic elements of Cr. The confined aquifer presented greater health risks than the phreatic aquifer. Consequently, controlling the levels of F and Cr in multi-layered aquifers is key to reducing health risks. These findings provide valuable insights into protecting groundwater from contamination by potentially toxic elements in multi-layered aquifers worldwide.

Dissolved trace elements in the upper reaches of Lancang River, southeast Qinghai-Tibet Plateau: Current status and distribution, risk and source

The environmental impact of dissolved trace element (DTE) pollution is becoming increasingly well understood. The southeastern edge of the Qinghai-Tibet Plateau is a region with high environmental vulnerability, making it highly susceptible to various anthropogenic disturbances. The Lancang River (LCR), as the most representative river in this area, serving as the largest international river in Southeast Asia, the LCR is crucial for the downstream regions, supporting fisheries, agriculture, and even economic development, earning it the title of the "water tower" for these areas. This study mainly focuses on the upstream unbuilt dam area. In this study, we gathered 25 surface water samples from the upstream of the LCR and examined the geographical distribution, primary sources, and associated human health risks of seven dissolved trace elements (Mn, Cr, Fe, Co, Cu, Ni, Zn). Cr, Fe, Co, Ni, and Cu in the main stream are primarily associated with natural sources, including rock weathering, soil erosion, and similar processes. Mn, however, predominantly originates from human activities such as industrial construction and road transportation. Zn mainly originates from the specialized fertilizers used for highland barley in plateau regions. Almost all DTE concentrations exhibited a downward trend from upstream to downstream. However, due to inputs from various sources along the way, there will be some abnormal points. The concentrations of DTE in the study area were within the acceptable limits set by drinking water standards in China, the USA, and the WHO. Overall, the water quality in this region is considered good. The results from a Monte Carlo simulation assessing health risks indicate that the non-carcinogenic health risk caused by DTE in the upper reaches of Lancang River was very weak. The primary pathway through which DTE can harm human health is ingestion, with children being particularly vulnerable. These findings offer a critical scientific basis for understanding the migration of DTE in aquatic environments and for the ecological stewardship of the Lancang River.

Unraveling the impact of high arsenic, fluoride and microbial population in community tubewell water around coal mines in a semiarid region: Insight from health hazards, and geographic information systems

High arsenic (As), fluoride (F-), and microbial pathogens coexist in semiarid conditions afflicting > 240 million people worldwide including Pakistan. Groundwater quality has declined due to geogenic and manmade activities providing suitable ground for ubiquity, bioavailability, and toxicity of contaminants. We tested the health hazard, distribution, and apportionment of As, F-, and microbes in groundwater around coal mines in Quetta, Pakistan. The range of As, and F- concentrations in groundwater were 0.2-16.6 µg/L, 0.4-18.5 mg/L. Both, As and F- correlate with high HCO3-, pH, Na+, SO42-, Fe, and Mn, and negatively with Ca2+ water. The coalfield showed many folds higher As 15.8-28.5 µg/L, and F- 10.8-34.5 mg/L compared to groundwater-wells. Geochemical phases revealed saturation of groundwater with calcite, dolomite, fluorite, gypsum, and undersaturation with halite-mirabilite, and arsenopyrite minerals. The positive matrix factorization (PMF) model assessed five-factor solutions: geogenic, industrial, coal mining, sulfide & fluoride-bearing mineral-dissolution, and agriculture pollution delivered As, F-, and microbial contamination. About 24.6 % and 64.4 % of groundwater samples exceeded the WHO guidelines of As 10 µg/L, F- 1.5 mg/L. The carcinogenicity, and non-carcinogenicity of As, and F- were higher in children than adults. Therefore, health hazards in children are of great concern in achieving sustainable management goals.

Determining priority control factors for heavy metal management in urban road dust based on source-oriented probabilistic ecological-health risk assessment: A study in Xi'an during peak pollution season

Urban road dust (URD) is essential for transporting heavy metals (HMs), which can be a major danger to both the environment and human health. Moreover, URD has the potential to be carried into bodies of water, leading to contamination of the aquatic ecosystem. A study was conducted in Xi'an, a city in northwestern China known for high air pollution levels, during January 2024 - a period characterized by peak pollution due to frequent low wind speeds and temperature inversions. The research investigated the presence of 10 types of HMs (Cu, Zn, Cd, Cr, Pb, As, Ni, Hg, Co, and Mn) in URD. Findings revealed elevated levels of Cu, Zn, Cd, Cr, Pb, As, and Hg in URD compared to background levels. Hg showed the most significant contamination (moderate to heavy), followed by moderate contamination of Cd, and lower levels of As, Zn, and Cu. The main sources of HMs were traffic (58.2%), mixed natural and industrial (30.3%), and industrial (11.5%). The ecological risk in the area was deemed to be very high, primarily because of Hg and Cd. Based on probabilistic health risk assessments, it was determined that non-carcinogenic risks were deemed acceptable for all groups. Nevertheless, the possibility of carcinogenic risks should not be disregarded. Strategies for controlling ecological-health risks prioritize mixed natural and industrial sources, with a focus on Hg, Cd, and As in URD. The results offer a foundation for policymakers to create specific control strategies.

Enhancing source apportionment of carbon, nitrogen, and phosphorus through integrating PMF and observed source profiles in a subtropical river

Apportioning pollution sources under compound pollution conditions is challenging in river pollution source analysis. The positive matrix factorization (PMF) model is widely used to analyze river pollution sources. However, the identification of pollutants in this model relies primarily on the subjective experience of the researchers, leading to ineffective identification of different contaminants from similar sources. In this study, we propose a comprehensive deviation index (CDI) to quantitatively identify pollution source types based on the PMF and observed source profiles. Taking the subtropical Xizhijiang River Basin as a case study, we quantitatively identified the pollution sources and their contributions to dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) using observed water quality and pollution sources data. The results showed that the eight major pollutants in the study region exhibited significant positive correlations, indicating the similarity of pollutant sources in the watershed. The PMF model identified three primary pollution sources with coefficients of determination for observed versus predicted concentrations ranging from 0.60 to 0.98. The CDI unveiled that the watershed's three pollution sources were farmland, rural, and wastewater treatment plants (WTPs). Farmland emerges as the predominant contributor to DOC (68.04 %), TC (63.29 %), and TDP (44.51 %). Rural notably contributes to NH3-N, PO4 3-, TDP, and TN, with percentages of 86.37 %, 57.65 %, 41.40 %, and 30.45 %, respectively. WTPs significantly contribute to NO2 -, NO3 -, and TN, accounting for 71.81 %, 57.39 %, and 37.26 %, respectively. Incorporating source fingerprints into the PMF model, the CDI can accurately identify pollution sources, improve the interpretability of source identification, and mitigate uncertainty in the multiple-source unknown receptor model. These findings have immediate and practical implications for river ecosystem management and pollution control, providing a more effective method for identifying and addressing pollution sources.

Source to risk receptor transport and spatial hotspots of heavy metals pollution in peri-urban agricultural soils of the largest megacity in China

The traditional concentration-based health risk assessment of heavy metal (HMs) pollution in soil has often overlooked the initial loading and toxicity differences of HMs from various sources. This oversight hinders effective identification of the risky source, complicating precise risk management of soil HMs pollution. This study applied a source-oriented health risk assessment framework that integrates source profiling, exposure risk assessment, and spatial cluster analysis. Taking the Shanghai City, the largest megacity in China as a case, the findings revealed that overall environmental quality of peri-urban agricultural soil in Shanghai remains good, though 3.03 % of Cd concentrations exceeded the national reference standards. Industrial & traffic activities, primarily contributing Hg, Cd, and Pb, accounted for the highest proportion (44.3 %) of total metal concentrations and posed the greatest non-cancer risk (54.6 % for children and 53.1 % for adults). Notably, natural activities, mainly contributing Cr, ranked only third in concentration contribution (26.55 %) but induced the highest cancer risk (58.55 % for children and 57.08 % for adults). These findings suggest that sources with lower concentration contributions may still pose significant health risk. Integrating source apportionment with health risk assessment can more precisely identify the risky source and target areas for mitigating the human health hazards.

Machine learning-driven assessment of heavy metal contamination in the impounded lakes of China's South-to-North Water Diversion Project: Identifying spatiotemporal patterns and ecological risks

The Eastern Route of China's South-to-North Water Diversion Project (SNWDP-ER) traverses through impounded lakes that are potentially vulnerable to heavy metals (HMs) contamination although the understanding remains elusive. This study employed machine learning approaches, including super-clustering of Self-Organizing Map (SOM) and Robust Principal Component Analysis (RPCA), to elucidate the spatiotemporal patterns and assess ecological risks associated with HMs in the surface sediments of Gao-Bao-Shaobo Lake (GBSL) and Dongping Lake (DPL). We collected 184 surface sediments from 47 stations across the two important impounded lakes over four seasons. The results revealed higher HMs concentrations in the south-central GBSL and west-central DPL, with a notable increase in contamination in autumn. The comprehensive risk assessment, utilizing various indicators such as the Sediment Quality Guidelines (SQGs), Improved Potential Ecological Risk Index (IPERI), Geo-accumulation Index (Igeo), Contamination Factor (CF), and Enrichment Factor (EF), identified arsenic (As), cadmium (Cd), nickel (Ni), and chromium (Cr) as primary contaminants of concern. Positive Matrix Factorization (PMF) model, coupled with Spearman analysis, attributed over 70 % of HMs pollution to anthropogenic activities. This research provides a nuanced understanding of HMs pollution in the context of large-scale water diversion projects and offers a scientific basis for targeted pollution mitigation strategies.

Source-risk and uncertainty assessment of trace metals in surface sediments of a human-dominated seaward catchment in eastern China

Current total concentration-based methods for source attribution and risk assessment often overestimate metal risks, thereby impeding the formulation of effective risk management strategies. This study aims to develop a framework for source-specific risk assessment based on metal bioavailability in surface river sediments from a human-dominated seaward catchment in eastern China. Metal bioavailability was quantified using chemical fractionation results, and source apportionment was conducted using the positive matrix factorization (PMF) model. Risk assessment integrated these findings using two indices: the Potential Ecological Risk Index (PERI) and the Mean Probable Effect Concentration Quotient (mPEC-Q), with uncertainty addressed via Monte Carlo simulations. Results indicated that average total concentrations of Cu, Pb, Zn, Cr, Hg, Cd, and As exceeded their respective background levels by 1.63 to 15.00 times. The residual fraction constituted the majority, accounting for 53.84 % to 77.79 % of total concentrations, suggesting significant natural origins. However, source apportionment revealed a predominant contribution from anthropogenic activities, including industrial smelting, agricultural practices, and atmospheric deposition. The contributions were found to vary between 5.35 % and 40.03 % when the total concentration was adjusted to bioavailable content. Total concentration-based PERI/mPEC-Q assessments indicated high/moderate risk levels, decreasing to considerable/low risk levels with bioavailability adjustment. Hg and Cd were identified as priority metals. Further incorporating source appointment parameters into the risk assessment, industrial smelting was identified as the primary contributor, accounting for 66.06 % of total risk by total concentration and 65.63 % by bioavailability. This underscores the role of bioavailability in mitigating risk overestimation. Monte Carlo simulations validated industrial smelting as a major risk contributor. This study emphasizes the importance of considering bioavailability in the source-risk assessment of sediment-metals, crucial for targeted risk management in urbanized catchment areas.

Assessment of soil environmental capacity for heavy metals in Shantou City, Guangdong Province, China: source analysis and enrichment evaluation

Most studies assessing soil environmental capacity (EC) often overlook the impact of heavy metal sources. Analyzing the sources of heavy metals (HMs) provides a better understanding of regional environmental capacity characteristics and their dynamic changes. The current study focuses on the surface soil of Shantou, using 511 soil samples to assess the soil environmental capacity. Results indicate that the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in Shantou's surface soil are notable, with lead moderately enriched and other metals lightly enriched. The principal component analysis (PCA) identifies five primary sources of heavy metals: mixed natural and agricultural sources, mixed agricultural and industrial sources, industrial sources, mining sources, and quarrying sources. The primary source contributing significantly to soil HM concentrations in Shantou City is a complex interplay between natural geological processes and extensive agricultural practices. In terms of static environmental capacity, Zn, Cr, Ni, Pb, Cu, As, Hg, and Cd are ranked in descending order. The overall environmental capacity for heavy metals in the soil is at a medium level, influenced by geological backgrounds. However, regions such as Yanhong Town, Guiyu Town, and Chendian Town face lower environmental capacities due to comprehensive human activities, posing certain risks. This study provides a scientific reference for forecasting, controlling soil heavy metal pollution, and improving soil quality and environmental capacity in Shantou City.

Quantitative tracing the sources and human risk assessment of complex soil pollution in an industrial park

Pollution in industrial parks has long been characterized by complex pollution sources and difficulties in identifying pollutant origins. This study focuses on a typical industrial park consisting of 11 factories (F1-F11) including organic pigment, inorganic pigment, and chemical factories in Hunan Province, China, here, a total of 327 sample points were surveyed. Eight pollutants (Mn, Cd, As, Co, NH3-N, l, 1,2-Trichloroethane, chlorobenzene, and petroleum hydrocarbons) were classified as contaminants of concern (COCs). This study assessed the contributions of driving factors to the distribution of COCs in the soil. Pollutant source apportionment was conducted using positive matrix factorization (PMF) and random forest (RF). The results revealed that the main factors driving pollution are groundwater migration, non-compliant emissions, leaks during production, and interactions among pollutants. The primary pollution sources were four chemical factories and an inorganic pigment factory. Source 5 demonstrates significant correlations with TCA (29.6%), CB (30%), and As (31.6%). Two chemical factories (F7 and F10) are the most significant pollution source with a risk assessment contribution rate of more than 60%. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of COCs in industrial park. By utilizing the proposed research framework, decision-makers can effectively prioritize and address identified pollution sources.

Do the residual metals in multiple environmental media surrounding mines pose ecological and health risks? A case of an abandoned mining area in central south China

Despite effective mining environmental regulations, residual metal pollution persists, leading to significant ecological harm and posing substantial risks to human well-being. This study employed multiple-criteria methods to investigate the ecological and health risks caused by metals in multiple environmental media (e.g., arable soil, indoor dust, PM10, homegrown vegetables, and rice) around abandoned mine areas (MA) in central south China. The study also aimed to identify predominant risk factors and the main exposure pathway. The findings revealed that metal levels and risks in the environmental media surrounding the MA were significantly higher than those in the control areas (away from abandoned mines, CA). This indicates that the accumulation of metals in the environmental media surrounding the MA was attributed to the previous mining activities. Variations in metal content were observed among different environmental media in MA, with Cd from mining source being the primary pollutant in arable soil, indoor dust, PM10, and vegetables, while As from agricultural source was the main pollutant in rice. Additionally, the consumption of Cd-contaminated vegetables and As-contaminated rice emerged as the primary routes of health hazards for the local population, leading to significant non-carcinogenic and carcinogenic risks. Consequently, it is imperative for the government and mining companies to promptly establish risk control and remedial strategies for mitigating residual metal levels in multiple environmental media surrounding the MA.

Pollution characteristics and source apportionment of heavy metal(loid)s in soil and groundwater of a retired industrial park

Heavy metal(loid)s (HMs) pollution has become a common and complex problem in industrial parks due to rapid industrialization and urbanization. Here, soil and groundwater were sampled from a retired industrial park to investigate the pollution characteristics of HMs. Results show that Ni, Pb, Cr, Zn, Cd, and Cu were the typical HMs in the soil. Source analysis with the positive matrix factorization model indicates that HMs in the topsoil stemmed from industrial activities, traffic emission, and natural source, and the groundwater HMs originated from industrial activities, groundwater-soil interaction, groundwater-rock interaction, and atmosphere deposition. The sequential extraction of soil HMs reveals that As and Hg were mainly distributed in the residue fraction, while Ni, Pb, Cr, Zn, Cd, and Cu mainly existed in the mobile fraction. Most HMs either in the total concentration or in the bioavailable fraction preferred to retain in soil as indicated by their high soil-water partitioning coefficients (Kd), and the Kd values were correlated with soil pH, groundwater redox potential, and dissolved oxygen. The relative stable soil-groundwater circumstance and the low active fraction contents limited the vertical migration of soil HMs and their release to groundwater. These findings increase our knowledge about HMs pollution characteristics of traditional industrial parks and provide a protocol for HMs pollution scrutinizing in large zones.

Health Risk Assessment of Heavy Metals in Agricultural Soils Based on Multi-Receptor Modeling Combined with Monte Carlo Simulation

The spatial characteristics, pollution sources, and risks of soil heavy metals were analyzed on Hainan Island. The results showed that the heavily polluted points accounted for 0.56%, and the number of mildly and above polluted points accounted for 15.27%, respectively, which were mainly distributed in the northern part of the study area. The principal component analysis-absolute principal component score-multiple linear regression (APCS-MLR) and the positive matrix factorization (PMF) revealed four sources of heavy metals: agricultural pollution sources for cadmium, (Cd), industrial and mining pollution sources for arsenic, (As), transportation pollution sources for zinc and lead (Zn and Pb), and natural pollution sources for chromium, nickel, and copper (Cr, Ni, and Cu). The human health risk assessment indicated that the average non-carcinogenic risk (HI) for both adults and children was within the safe threshold (<1), whereas Cr and Ni posed a carcinogenic risk (CR) to human health. In addition, the total non-carcinogenic risk (THI) indicated that heavy metals posed a potential non-carcinogenic risk to children, while the total carcinogenic risk (TCR) remained relatively high, mainly in the northern part of the study area. The results of the Monte Carlo simulation showed that the non-carcinogenic risk (HI) for all heavy metals was <1, but the total non-carcinogenic risk index (THI) for children was >1, indicating a potential health risk above the safe threshold. Meanwhile, nearly 100% and 99.94% of the TCR values exceeded 1 × 10-4 for children and adults, indicating that Cr and Ni are priority heavy metals for control. The research results provide the necessary scientific basis for the prevention and control of heavy metals in agricultural soils.

Spatial distribution and factors influencing the accumulation of toxic metals in soils in mountain agroecosystems, Rio De Janeiro, Brazil

The concentrations of toxic metals (TM) were analyzed in 498 samples of agricultural soils used for intensive vegetable cultivation in the watershed of Barracão dos Mendes, Brazil. The goal of this study was to characterize the distribution of these elements and the main natural and anthropogenic factors affecting their accumulation. In general, the average concentrations of TM were higher than the reference quality values for cultivated soils in the region, with the exception of Cr, Co, Ni and Mn, and the average concentration of Cd was ten times greater. Three sources of variation in the distribution of TM concentrations were identified: one related to topographic relief, another related to lithology, and one related to the massive use of agrochemicals. These factors contributed to TM accumulation in the soil; moreover, the transport of toxic metal-enriched clay by runoff resulted in higher concentrations of these elements in the lower parts of the slope. The long-term application of massive amounts of fertilizers and pesticides resulted in the accumulation of Cr, Cu, Zn, Ni, Pb and Cd in the vegetable cultivation soils and promoted the enrichment of macronutrients, mainly P and K. Moreover, the spatial distribution of TM in the agricultural soils of this mountain agroecosystem was affected by intensive vegetable cultivation, which altered the natural TM distribution dynamics determined by variations in topographic relief and lithology. In intensive cultivation areas, the TM distribution was also influenced by soil management practices such as tillage along the slope direction and massive mineral and organic fertilization.

A study on source identification of contaminated soil with total petroleum hydrocarbons (aromatic and aliphatic) in the Ahvaz oil field

The oil industry in Khuzestan province (Southwest Iran) is one of the main reasons contributing to the pollution of the environment in this area. TPH, including both aromatic and aliphatic compounds, are important parameters in creating pollution. The present study aimed to investigate the source of soil contamination by TPH in the Ahvaz oil field in 2022. The soil samples were collected from four oil centers (an oil exploitation unit, an oil desalination unit, an oil rig, and a pump oil center). An area outside the oil field was determined as a control area. Ten samples with three replicates were taken from each area according to the standard methods. Aromatic and aliphatic compounds were measured by HPLC and GC methods. The positive matrix factorization (PMF) model and isomeric ratios were used to determine the source apportionment of aromatic compounds in soil samples. The effects range low and effects range median indices were also used to assess the level of ecological risk of petroleum compounds in the soil samples. The results showed that Benzo.b.fluoranthene had the highest concentration with an average of 5667.7 ug/kg in soil samples in the Ahvaz oil field. The highest average was found in samples from the pump oil center area at 7329.48 ug/kg, while the lowest was found in control samples at 1919.4 ug/kg-1. The highest level of aliphatic components was also found in the pump oil center, with a total of 3649 (mg. Kg-1). The results of source apportionment of petroleum compounds in soil samples showed that oil activities accounted for 51.5% of the measured PAHs in soil. 38.3% of other measured compounds had anthropogenic origins, and only 10.1% of these compounds were of biotic origin. The results of the isomeric ratios also indicated the local petroleum and pyrogenic origin of PAH compounds, which is consistent with the PMF results. The analysis of ecological risk indices resulting from the release of PAHs in the environment showed that, except for fluoranthene, other PAHs in the oil exploitation unit area were above the effects range median level (ERM) and at high risk. The results of the study showed that soil pollution by total petroleum hydrocarbons (TPH), both aromatic and aliphatic, is at a high level, and is mainly caused by human activities, particularly oil activities.

Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China

Determining sources and spatial distributions of potentially toxic elements (PTEs) is a crucial issue of soil pollution survey. However, uncertainty estimation for source contributions remains lack, and accurate spatial prediction is still challenging. Robust Bayesian multivariate receptor model (RBMRM) was applied to the soil dataset of Qingzhou City (8 PTEs in 429 samples), to calculate source contributions with uncertainties. Multi-task convolutional neural network (MTCNN) was proposed to predict spatial distributions of soil PTEs. RBMRM afforded three sources, consistent with US-EPA positive matrix factorization. Natural source dominated As, Cr, Cu, and Ni contents (78.5 %∼86.1 %), and contributed 37.1 %, 61.0 %, and 65.9 % of Cd, Pb, and Zn, exhibiting low uncertainties with uncertainty index (UI) < 26.7 %. Industrial, traffic, and agricultural sources had significant influences on Cd, Pb, and Zn (30.2 %∼61.9 %), with UI < 39.3 %. Hg originated dominantly from atmosphere deposition (99.1 %), with relatively high uncertainties (UI=87.7 %). MTCNN acquired satisfactory accuracies, with R2 of 0.357-0.896 and nRMSE of 0.092-0.366. Spatial distributions of As, Cd, Cr, Cu, Ni, Pb, and Zn were influenced by parent materials. Cd, Hg, Pb, and Zn showed significant hotspot in urban area. This work conducted a new approach exploration, and practical implications for soil pollution regulation were proposed.

Distinguishing the contributions of different smelting emissions to the spatial risk footprints of toxic elements in soil using PMF, Bayesian isotope mixing models, and distance-based regression

Toxic element pollution of soils emanating from smelting operations is an escalating global concern due to its severe impact on ecosystems and human health. In this study, soil samples were collected and analyzed to quantify the risk contributions and delineate the spatial risk footprints from smelting emissions for 8 toxic elements. A comprehensive health risk contribution and delineation framework was utilized, consisting of Positive matrix factorization (PMF), spatial interpolation, an advanced Bayesian isotope mixing model via Mixing Stable Isotope Analysis in R (MixSIAR), and distance-based regression. The results showed that the mean concentrations of As, Cd, Cu, Hg, Pb, and Zn exceeded the background levels, indicating substantial contamination. Three sources were identified using the PMF model and confirmed by spatial interpolation and MixSIAR, with contributions ranked as follows: industrial wastewater discharge and slag runoff from the smelter site (48.9 %) > natural geogenic inputs from soil parent materials (26.7 %) > atmospheric deposition of dust particles from smelting operations (24.5 %). Among the identified sources, smelter runoff posed the most significant risk, accounting for 97.9 % of the non-carcinogenic risk (NCR) and 59.9 % of the carcinogenic risk (CR). Runoff also drove NCR and CR exceedances at 7.8 % and 4.7 % of sites near the smelter, respectively. However, atmospheric deposition from smelting emissions affected soils across a larger 0.8 km radius. Although it posed lower risks, contributing just 1.1 % to NCR and 22.6 % to CR due to the limited elevation of toxic elements, deposition reached more distant soils. Spatial interpolation and distance-based regression delineated high NCR and CR exposure hotspots within 1.4 km for runoff and 0.8 km for deposition, with exponentially diminishing risks at further distances. These findings highlight the need for pathway-specific interventions that prioritize localized wastewater containment and drainage controls near the smelter while implementing broader regional air pollution mitigation measures.

Characteristics and correlation analysis of heavy metal distribution in China's freshwater aquaculture pond sediments

The concentration of heavy metals (HMs) in aquaculture pond sediments significantly affects aquatic food safety and environmental quality. The contamination characteristics, drivers and potential sources of HMs in typical bulk freshwater aquaculture pond sediments in major provinces of China were systematically investigated using a variety of methods and models. Specifically, 130 surface sediment samples were collected from the study area, and the geoaccumulation index (Igeo) and potential ecological risk index (RI) were used to jointly evaluate the characteristics of the HMs. Spearman's correlation and redundancy analysis revealed the main drivers of the HMs. Additionally, the positive matrix factorization (PMF) model and absolute principal component score-multiple linear regression (APCS-MLR) model were used to identify the sources of HMs. The results revealed that the pond sediments were safe for fish culture in most of the study areas. Aquafeed protein content is an important driver of HM concentrations in sediments. The total organic carbon (TOC) content, percentage of clay particles, and pH of the aquaculture pond sediments determined the sediment HMs enrichment abilities as 13.6 %, 52 %, and 9.8 %, respectively. Cd, a significantly enriched pollutant, posed a greater ecological risk than the other five HMs (Cr, Cu, Zn, As, and Pb). Three sources of HMs were identified, including agricultural activity (e.g., aquafeeds, pesticides, and fertilizers), industrial production, and natural sources, with contributions of 44.29 %, 36.66 %, and 19.05 %, respectively. This study provides a scientific basis for minimizing the input and accumulation of HMs in freshwater aquaculture pond sediments, and this can provide insights into the prevention and control of the ecological risks posed by HMs.

Identification of toxic metal contamination in surface sediments of the Xiaoqing River under a long-term perspective (1996-2020): Risks, sources and driving factors

The contamination of sediments by toxic metals poses a significant threat to both river ecosystems and human health. In this study, the geo-accumulation index (Igeo), biotoxicity evaluation method, and potential ecological risk index (RI) were employed to analyze the contamination level, biotoxicity risk, and potential ecological risk of toxic metals in surface sediments of the Xiaoqing River. To identify toxic metal sources, Spearman correlation and principal component analysis with multiple linear regression analysis (PCA-MLR) were employed. Additionally, redundancy analysis (RDA) was utilized to investigate potential driving factors affecting toxic metal accumulation in sediments. The results revealed that the levels of the five investigated metals (Cr, Pb, As, Hg, and Cd) showed constant fluctuations during the period 1996-2020. The midstream was found to be more polluted than the upstream and downstream. In the research area, Hg was identified as the primary contaminant with high levels of contamination, posing a biotoxicity risk and potential ecological risk. Pollution sources were identified for two periods: A (1996-2010) and B (2011-2020), with industrial, agricultural, traffic, and natural sources being the main contributors. During period A, industrial sources accounted for the highest proportion (40.8%), followed by agricultural sources (36.6%), and geological natural sources (22.6%). During period B, agricultural sources accounted for the highest proportion (42%), followed by industrial and traffic sources (32.4%), and geological natural sources (25.6%). The distribution of toxic metals in the basin was significantly influenced by water pH, sediment organic matter, population density, and per capita GDP. The study results provide fundamental data for preventing pollution and managing water resources contaminated with toxic metals in the sediments of the Xiaoqing River in Jinan. Additionally, it serves as a reference for analyzing related ecological and environmental issues in the basin.

Homology and heterogeneity of soil trace elements of coal power production bases in arid and semi-arid areas of Northwest China

Coal power activities could cause regional fluctuations of trace elements, but the distribution information of these trace elements in arid and semi-arid areas is insufficient. In this study, the soil trace elements (As, B, Be, Cd, Co, Cr, Cu, Fe, Ga, Ge, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Ti, Tl, and Zn) of Ningdong Coal Power Production Base in China were monitored. Results showed that the concentrations of B, Tl, Mn, Pb, Cr, K, Cu, and Co exceeded background values. The maximum risk index reached 265.66, while the trace elements posed a cancer risk to children. Combining correlation analyses (CA), principal component analysis (PCA), and positive matrix factorization (PMF) techniques, it indicated that trace elements were mainly coming from coal combustion (34.15%), livestock farming (17.44%), traffic emissions (12.42%), and natural factors (35.99%). This study reveals the sources and potential ecological risks of soil trace elements in the Ningdong Coal and Power Production Base. It provides a scientific basis for developing targeted environmental management measures and reducing human health risks.

Quantification of sources and potential risks of cadmium, chromium, lead, mercury and arsenic in agricultural soils in a rapidly urbanizing region of southwest China: the case of Chengdu

Rapid urbanization a major factor affecting heavy metal contamination on suburban agricultural soils. In order to assess the dynamic contamination of heavy metals in soil from agricultural land bordering a rapidly urbanizing area and the transfer of human health risks from contaminants in this process, 186 and 293 soil samples from agricultural land in suburban Chengdu were collected in September 2008 and September 2017, respectively. Several indicators, such as the integrated pollution index (PI) and the potential ecological risk index (RI), were employed for analyzing the heavy metal contamination levels, and the APCS-MLR receptor model were applied for analyzing the heavy metal sources. As a result, mean concentrations for five elements did not exceed the national soil pollution risk screening values in the two periods mentioned above. Nemerow's composite contamination index revealed an increase in soil contamination of arable land after 10 years of urbanization, with 3.75 and 1.02% of light and moderate sample plots, respectively, by 2017. The assessment for potential ecological risk indicated an increased level of eco-risk to high for most of the sample plots. Based on the APCS-MLR model, the origin and contribution to the five elements varied considerably between the two periods mentioned above. Among them, soil Pb changed from "industrial source" to "transportation source," soil Cr changed from "natural source" to "transportation source," and As and Hg changed from "industrial source" to "transportation source." As and Hg were associated with agricultural activities in both periods, and Cd was derived from industrial activities in both periods. The study suggests that inhalation has become a major contributor to non-cancer health risks in urbanization, unlike intake routes in previous periods, and that the increase in cancer risk is mainly due to children's consumption of agricultural products with As residues. The change in the main source of As to "transportation" also indicates a decrease in air quality during urbanization and the development of the transportation industry. This study provides a reference for the governments of rapidly urbanizing cities to formulate relevant highway and agricultural policies to safeguard the health of the people based on the current situation.

A Modified Model for Quantitative Heavy Metal Source Apportionment and Pollution Pathway Identification

Current source apportionment models have successfully identified emission sources and quantified their contributions. However, when being utilized for heavy metal source apportion in soil, their accuracy needs to be improved, regarding migration patterns. Therefore, this work intended to improve the pre-existing principal component analysis and multiple linear regression with distance (PCA-MLRD) model to effectively locate pollution pathways (traffic emissions, irrigation water, atmospheric depositions, etc.) and achieve a more precise quantification. The dataset of soil heavy metals was collected from a typical area in the Chang-Zhu-Tan region, Hunan, China in 2021. The identification of the contribution of soil parent material was accomplished through enrichment factors and crustal reference elements. Meanwhile, the anthropogenic emission was identified with principal component analysis and GeoDetector. GeoDetector was used to accurately point to the pollution source from a spatial differentiation perspective. Subsequently, the pollution pathways linked to the identified sources were determined. Non-metal manufacturing factories were found to be significant anthropogenic sources of local soil contamination, mainly through rivers and atmospheric deposition. Furthermore, the influence of irrigation water on heavy metals showed a more pronounced effect within a distance of 1000 m, became weaker after that, and then gradually disappeared. This model may offer improved technical guidance for practical production and the management of soil heavy metal contamination.

Source-specific probabilistic health risk assessment of heavy metals in surface water of the Yangtze River Basin

The detrimental effects of heavy metal accumulation on both ecosystems and public health have raised widespread concern. Source-specific risk assessment is crucial for developing effective strategies to prevent and control heavy metal contamination in surface water. This study aims to investigate the contamination characteristics of heavy metals in the Yangtze River Basin, identifying the pollution sources, assessing the risk levels, and further evaluating the health risks to humans. The results indicated that the average concentrations of heavy metals were ranked as follows: zinc (Zn) > arsenic (As) > copper (Cu) > chromium (Cr) > cadmium (Cd) > nickel (Ni) > lead (Pb), with average concentrations of 38.02 μg/L, 4.34 μg/L, 2.53 μg/L, 2.10 μg/L, 1.17 μg/L, 0.84 μg/L, and 0.32 μg/L, respectively, all below the WHO 2017 standards for safe drinking water. The distribution trend indicates higher concentrations in the upper and lower reaches and lower concentrations in the mid-reaches of the river. By integrating the Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) receptor model and Positive Matrix Factorization (PMF) model, the main sources of heavy metals were identified as industrial activities (APCS-MLR: 41.3 %; PMF: 42.1 %), agricultural activities (APCS-MLR: 30.1 %; PMF: 27.4 %), and unknown mix sources (APCS-MLR: 29.1 %; PMF: 30.4 %). The calculation of the hazard index (HI) for both children and adults was <1, indicating no non-carcinogenic or carcinogenic risks. Based on the source-specific risk assessment, agricultural activities contribute the most to non-carcinogenic risks, while industrial activities pose the greatest contribution to carcinogenic risks. This study offers a reference for monitoring heavy metals and controlling health risks to residents, and provides crucial evidence for the utilization and protection of surface water in the Yangtze River Basin.

Pollution sources and risk assessment of potentially toxic elements in soils of multiple land use types in the arid zone of Northwest China based on Monte Carlo simulation

The concentration of potentially toxic elements (PTEs) in soils of different land-use types varies depending on climatic conditions and human. Topsoil samples were collected in Northwest China to investigate PTE pollution and risk in different land uses, and thereby estimate the risk of various pollution sources. The results showed that human activity had an impact on PTE concentrations in the study area across all land use types, with farmland, grassland, woodland, and the gobi at moderate pollution levels and the desert at light pollution levels. Different PTE sources pose different risks depending on the land-use type. Apart from deserts, children are exposed to carcinogenic risk from a variety of sources. A mixed natural and agricultural source was the main source of public health risk in the study area, contributing 38.7% and 39.0% of the non-carcinogenic and 40.7% and 35.5% of the carcinogenic risks, respectively. Monte Carlo simulations showed children were at a higher health risk from PTEs than adult s under all land uses, which ranked in severity as farmland > woodland > grassland > gobi > desert. As and Ni has a higher probability of posing both a non-carcinogenic and a carcinogenic risk to children. Sensitivity analysis showed that the contribution of parameters to the assessment model of PTEs exhibited the following contribution pattern: concentration > average body weight > ingestion rate > other parameters. The PTEs affecting the risk assessment model were not common among different land use types, where the importance distribution pattern of each parameter was basically the same in woodland, grassland, and farmland, and Ni contributed the most to carcinogenic risk. However, Cr contributed the most to the carcinogenic risk in the desert and gobi.

Characterization and childhood exposure assessment of toxic heavy metals in household dust under true living conditions from 10 China cities

Health hazards caused by metal exposure in household dust are concerning environmental health problems. Exposure to toxic metals in household dust imposes unclear but solid health risks, especially for children. In this multicenter cross-sectional study, a total of 250 household dust samples were collected from ten stratified cities in China (Panjin, Shijiazhuang, Qingdao, Lanzhou, Luoyang, Ningbo, Xi'an, Wuxi, Mianyang, Shenzhen) between April 2018 and March 2019. Questionnaire was conducted to gather information on individuals' living environment and health status in real-life situations. Multivariate logistic regression and principal component analysis were conducted to identify risk factors and determine the sources of metals in household dust. The median concentration of five metals in household dust from 10 cities ranged from 0.03 to 73.18 μg/g. Among the five heavy metals, only chromium in household dust of Mianyang was observed significantly both higher in the cold season and from the downwind households. Mercury, cadmium, and chromium were higher in the third-tier cities, with levels of 0.08, 0.30 and 97.28 μg/g, respectively. There were two sources with a contribution rate of 38.3 % and 25.8 %, respectively. Potential risk factors for increased metal concentration include long residence time, close to the motorway, decoration within five years, and purchase of new furniture within one year. Under both moderate and high exposure scenarios, chromium showed the highest level of exposure with 6.77 × 10-4 and 2.28 × 10-3 mg·kg-1·d-1, and arsenic imposed the highest lifetime carcinogenic risk at 1.67 × 10-4 and 3.17 × 10-4, respectively. The finding highlighted the priority to minimize childhood exposure of arsenic from household dust.

Sources apportionments of heavy metal(loid)s in the farmland soils close to industrial parks: Integrated application of positive matrix factorization (PMF) and cadmium isotopic fractionation

Understanding the source identification and distribution of heavy metal(loid)s in soil is essential for risk management. The sources of heavy metal(loid)s in farmland soil, especially in areas with rapid economic development, were complicated and need to be explored urgently. This study combined geographic information system (GIS) mapping, positive matrix factorization (PMF) model and cadmium (Cd) isotope fingerprinting methods to identify heavy metal(loid) sources in a typical town in the economically developed Yangtze River Delta region of China. Cd, As, Cu, Zn, Pb, Ni and Co in different samples were detected. The results showed that Cd was the most severely contaminated element, with an exceedance rate of 78.0 %. GIS mapping results indicated that the hotspot area was located in the northeastern area with prolonged operational histories of electroplating and non-ferrous metal smelting industries. The PMF model analysis also identified emissions from smelting and electroplating enterprises as the main sources of Cd in the soil, counted for 49.28 %, followed by traffic (25.66 %) and agricultural (25.06 %) sources. Through further isotopic analysis, it was found that in soil samples near the industrial park, the contribution of electroplating and non-ferrous metal smelting enterprises to cadmium pollution was significantly higher than other regions. The integrated use of various methodologies allows for precise analysis of sources and input pathways, offering valuable insights for future pollution control and soil remediation endeavors.

Lead Isotope Signatures and Source Identification of Heavy Metals in Vegetable Soils Irrigated with Swine Wastewater of Jiangxi Province, China

The rapid development of livestock and poultry industry in China has caused serious environment pollution problems. To understand the heavy metals accumulation and identify their sources, 7 heavy metals contents and lead isotope ratios were determined in 24 soil samples from vegetable fields irrigated with swine wastewater in Dongxiang County, Jiangxi Province, China. The results showed that the concentration of Cr, Ni, Cu, Zn, As, Cd and Pb in the swine wastewater irrigated vegetable soils varied from 38.5 to 86.4, 7.57 to 30.6, 20.0 to 57.1, 37.5 to 174, 9.18 to 53.1, 0.043 to 0.274 and 12.8 to 37.1 mg/kg, respectively. The soils were moderately to heavily polluted by As, moderately polluted by Cr, Ni, Cu, Zn and Cd, and unpolluted to moderately polluted by Pb. Sampling soils were classified as moderately polluted according to the Nemerow comprehensive pollution index. Lead isotope and Principal Component Analysis (PCA) analysis indicated that swine wastewater irrigation and atmospheric deposition were the primary sources of the heavy metals.

Core-shell design of UiO66-Fe3O4 configured with EDTA-assisted washing for rapid adsorption and simple recovery of heavy metal pollutants from soil

The coupling of washing with adsorption process can be adopted for the treatment of soils contaminated with heavy metals pollution. However, the complex environment of soil and the competitive behavior of leaching chemicals considerably restrain adsorption capacity of adsorbent material during washing process, which demands a higher resistance of the adsorbents to interference. In this study, we synthesized strongly magnetic, high specific surface area (573.49 m2/g) UiO66 composites (i.e., UiO66-Fe3O4) using hydrothermal process. The UiO66-Fe3O4 was applied as an adsorbent during the ethylene diamine tetraacetic acid (EDTA)-assisted washing process of contaminated soil. The incorporation of UiO66-Fe3O4 results in rapid heavy metal removal and recovery from the soil under low concentrations of washing agent (0.001 mol/L) with reduced residual heavy metal mobility of soil after remediation. Furthermore, UiO66-Fe3O4 can quickly recollect by an external magnet, which offers a simple and inexpensive recovery method for heavy metals from contaminated soil. Overall, UiO66-Fe3O4 configuration with EDTA-assisted washing process showed opportunities for heavy metals contaminated sites.

Historical construction, quantitative source identification and risk assessment of heavy metals contamination in sediments from the Pearl River Estuary, South China

Historical reconstruction of heavy metals (HMs) contamination in sediments is a key for understanding the effects of anthropogenic stresses on water bodies and predicting the variation trends of environmental state. In this work, eighteen sediment cores from the Pearl River Estuary (PRE) were collected to determine concentrations and geochemical fractions of HMs. Then, their potential sources and the relative contributions during different time periods were quantitatively identified by integrating lead-210 (210Pb) radioisotope dating technique into positive matrix factorisation (PMF) method. Pollution levels and potential ecological risks (PERs) caused by HMs were accurately assessed by enrichment factors (EF) based on establishment of their geochemical baselines (GCBs) and multiparameter evaluation index (MPE). HMs concentrations generally showed a particle size- and organic matter-dependent distribution pattern. During the period of 1958-1978, HMs concentrations remained at low levels with agricultural activities and natural processes being identified as the predominant sources and averagely contributing >60%. Since the reform and opening-up in 1978, industrial and traffic factors become the primary anthropogenic sources of HMs (such as Cu, Zn, Cd, Pb, Cr, and Ni), averagely increasing from 22.1% to 28.1% and from 11.6% to 23.4%, respectively. Conversely, the contributions of agricultural and natural factors decreased from 37.0% to 28.5% and from 29.3% to 20.0%, respectively. Subsequently, implementation of environmental preservation policies was mainly responsible for the declining trend of HMs after 2010. Little enrichment of sediment Cu, Zn, Pb, Cr and Ni with EFs (0.15-1.43) was found in the PRE, whereas EFs of Cd (1.16-2.70) demonstrated a slight to moderate enrichment. MPE indices of Cu (50.7-252), Pb (52.0-147), Zn (35.5-130), Ni (19.6-71.5), Cr (14.2-68.8) and Cd (0-9.90) highlighted their potential ecological hazards due to their non-residual fractions and anthropogenic sources.

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

Tracing and source fingerprinting of metals from the southern coastal sediments in Bangladesh

Trace element pollution from anthropogenic sources is increasingly widespread. This pollution in terrestrial environments threatens agricultural crop production, while in aquatic environments, it threatens fish cultivation. The contamination of these crucial food sources raises significant concerns regarding food safety, security, and its potential adverse effects on human health. Coastal areas are particularly vulnerable to heavy metal pollution due to their proximity to industrial and urban centres, as well as their susceptibility to contamination from marine sources. In attempting to identify the sources of heavy metals (As, Cu, Cr, Cd, Fe, Hg, Mn, Ni, Pb, and Zn) and measure their contributions, we collected soil samples from thirty sites along the three coastal districts (Patuakhali, Barguna, and Bhola) in Bangladesh. Using atomic absorption spectroscopy, heavy metal concentrations in soil samples were measured and three receptor models (PMF, PCA-MLR, and UNMIX) were applied to detect their sources. Pairwise correlation analysis of metal concentrations in 30 sites across 3 coastal districts showed all possible patterns, including both significant and insignificant positive and negative relationships between different metals, except for As and Hg which did not display any significant relationships with other metals. The concentrations of Cu, Fe, Mn, Ni, and Zn exceed the US-EPA sediment quality standard. The applied PCA-MLR, PMF, and UNMIX models identified several sources of heavy metal contamination, including (i) mixed anthropogenic and natural activities: contribution of 59%, 37%, and 43%, and (ii) vehicle emissions: contribution of 23%, 26% and 29%. The recognized metal sources should be prioritised to avoid the discharge of poisonous pollutants from anthropogenic factors and any possible future exposure. This study's findings have implications for ongoing monitoring and management of heavy metal contamination in coastal environments to mitigate potential health and ecological impacts and can inform policy development and management strategies.

Heavy metal pollution in agricultural soils from surrounding industries with low emissions: Assessing contamination levels and sources

The potential for heavy metal (HM) pollution in agricultural soils adjacent to industries with elevated HM emissions has long been recognized. However, industries with relatively lower levels of HM emissions, such as alumina smelting and glass production, may still contribute to the pollution of surrounding agricultural soils through continuous, albeit low-level, emissions. Despite this, this issue has not garnered adequate attention thus far. Therefore, this study aimed to assess the extent of HM pollution in agricultural soils adjacent to an alumina smelting and a glass production factory, identifying contamination levels and potential sources through the analysis of input fluxes, isotope fingerprints, and receptor models. Results showed moderate cadmium (Cd) contamination in surface soil, exceeding standards at a rate of 86.36 %. Further analysis revealed that atmospheric deposition was the primary route for Cd input in both paddy fields (89.20 %) and dryland soils (91.61 %). Additionally, the δ114/110Cd values in surface soils indicated that dust played a role in influencing Cd levels in distant surface soils, while raw materials and slags were identified as primary sources near the factory. Industrial sources were considered the primary contributors of Cd in soil accounting for approximately 73.38 % and 82.67 %, respectively, according to the positive matrix factorization model (PMF) and absolute principal component scores-multiple linear regression model (APCS-MLR). Overall, this study underscores the importance of monitoring HMs from industries with relatively low emissions and provides a scientific basis for effectively managing HMs pollution in agricultural soils, ensuring the preservation of agricultural soil quality.

Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui

In this study, stone coal mines in the lower reaches of the Zijiang River were adopted as the research object. To analyze the spatial distribution, sources, and health risks of heavy metals in the surrounding soil of stone coal mines, 82 topsoil samples were collected in the study area, and the contents of 8 heavy metals including Cd, Hg, As, Cr, Pb, Cu, Ni, and Zn were determined. The spatial distribution of heavy metals was analyzed using ArcGIS, and the pollution sources of heavy metals were identified using Positive matrix factorization (PMF). Then, Monte Carlo and health risk assessment models were used to evaluate the health risks of different populations. The results showed that the average content of heavy metals followed the order of Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, and the contents of all heavy metals were higher than the soil background values of Hunan Province. The high-value areas of heavy metals content were mostly concentrated in the central region close to areas with a notable concentration of stone coal mines. PMF identified four pollution sources, namely, mining activities (26.9%), atmospheric deposition (18.8%), natural sources (32.8%) and agricultural sources (21.5%). The carcinogenic and non-carcinogenic risks for children were higher than those for adults, with As and Cd posing higher carcinogenic risks to children. Based on the source of health risks, it was determined that the health risks could be primarily attributed to agricultural sources, and As was the main heavy metal causing health risks. This study provides theoretical support for treating heavy metal pollution in mining basins.

Characteristics, source analysis, and health risk assessment of potentially toxic elements pollution in soil of dense molybdenum tailing ponds area in central China

The issue of potentially toxic elements (PTEs) contamination of regional soil caused by mining activities and tailings accumulation has attracted wide attention all over the world. The East Qinling is one of the three main molybdenum mines in the world, and the concentration of PTEs such as Hg, Pb and Cu in the slag is high. Quantifying the amount of PTEs contamination in soil and identifying potential sources of contamination is vital for soil environmental management. In the present investigation, the pollution levels of 8 PTEs in the Qinling molybdenum tailings intensive area were quantitatively identified. Additionally, an integrated source-risk method was adopted for resource allocation and risk assessment based on the PMF model, the ecological risk, and the health risk assessment model. The mean concentrations of Cu, Ni, Pb, Cd, Cr, Zn, As, and Hg in the 80 topsoil samples ranged from 0.80 to 13.38 times the corresponding background values; notably high levels were observed for Pb and Hg. The source partitioning results showed that PTEs were mainly affected by four pollution sources: natural and agricultural sources, coal-burning sources, combined transport and mining industry sources, and mining and smelting sources. The health risk assessment results revealed that the risks of soil PTEs for adults are acceptable, while the risks for children exceeded the limit values. The obtained results will help policymakers to obtain the sources of PTEs of tailing ponds intensive area. Moreover, it provides priorities for the governance of subsequent pollution sources and ecological restoration.

WITHDRAWN: Trace metals translocation from soil to plants: Health risk assessment via consumption of vegetables in the urban sprawl of a developing country

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Source identification and exposure risk management for soil arsenic in urban reclamation areas with high background levels: A case study in a coastal reclamation site from the Pearl River Delta, China

Urbanization involving the excavation and reuse of arsenic-bearing geological materials may pose human health risks. We investigated the distribution and sources of soil arsenic at a coastal reclamation site in the Pearl River Delta, China, and proposed risk management strategies. Analysis of 899 soil samples revealed an average of 58.97 mg/kg arsenic, with a maximum of 1450 mg/kg, mainly in fill material obtained from a local island. Integrative analysis combining reclamation history, regional geology, and bedrock mineralogy conclusively identified hydrothermally altered arsenic-bearing sulfide minerals within extensively fractured bedrock as the primary source of arsenic. Physical weathering and anthropogenic rock blasting produced discrete arsenic-rich particles that were directly transported into soils during land reclamation and accumulated to potential hazardous levels. Oral, dermal, and inhalation pathways were identified as primary exposures for future populations. Integrated engineering and institutional controls, coupled with long-term monitoring, were recommended to mitigate risks. The results highlight the importance of identifying specific geogenic and anthropogenic sources that contribute to heavy metal enrichment of soils in reclaimed areas where native bedrock naturally contains elevated level of metals, supporting evidence-based best practices for risk management and future land use.

Assessing heavy metal pollution in sediments from the northern margin of Chinese mangrove areas: Sources, ecological risks, and health impacts

With the rapid economic development of coastal cities, the discharge of substantial amounts of heavy metal pollutants poses a serious hazard to mangroves; however, the potential sources of heavy metals and the resulting health risks are not fully understood. In this study, we analyzed the contents, sources, and ecological and health risks of heavy metal contamination in mangrove sediments from the northern margin of China. The accumulation of heavy metals in mangroves was primarily driven by five potential sources, namely agricultural (33.5 %), natural sources (21.3 %), industrial (19.1 %), aquaculture (14.3 %), and traffic (11.8 %). The assessment of health risks using a probabilistic approach demonstrated that noncarcinogenic risks were within acceptable limits for all populations. It was worth noting that both noncarcinogenic and carcinogenic risks were greater in children than in adults. Analysis of source-oriented health risks revealed that agricultural sources and As and Cd were priority sources and elements of pollution requiring attention.

A study of environmental pollution and risk of heavy metals in the bottom water and sediment of the Chaohu Lake, China

Most of the existing research for heavy metals in water at present is focusing on surface water. However, potential environmental risk of heavy metals in the bottom water of lakes cannot be ignored. In this study, the content, distribution, and speciation of nine heavy metals (As, V, Cr, Co, Ni, Cu, Zn, Cd, and Pb) in the bottom water and sediment of Chaohu Lake were studied. Some pollution assessment methods were used to evaluate the environmental effect of heavy metals. Positive matrix factorization was conducted to investigate the potential sources of heavy metals in sediment. The contents of heavy metals in the bottom water of Chaohu Lake mean that its environmental pollution can be ignored. In sediment, Cd and Zn have showed stronger ecological risk. pH and redox potential are more likely to affect the stability of heavy metals in the bottom water of Chaohu Lake during the dry reason. Industrial sources (16%) are no longer the largest source of heavy metal pollution; traffic sources (33.6%) and agricultural sources (23.4%) have become the main sources of pollution at present. This study can provide some support and suggestions for the treatment of heavy metals in lakes.

Road dust exposure and human corneal damage in a plateau high geological background provincial capital city: Spatial distribution, sources, bioaccessibility, and cytotoxicity of dust heavy metals

Ocular surface diseases are common in the plateau city, Kunming China, the continued daily exposure to heavy metals in dust may be an important inducement. In this study, the 150 road dust samples from five functional areas in Kunming were collected. The concentrations, distribution, possible sources, and bioaccessibility of heavy metals were analyzed. The adverse effects of dust extracts on human corneal epithelial cells and the underlying mechanisms were also assessed. The concentrations (mg·kg-1) of As (19.1), Cd (2.67), Cr (90.5), Cu (123), Pb (78.4), and Zn (389) in road dust were higher than the soil background, with commercial and residential areas showing the highest pollution. Their bioaccessibility in artificial tears was As (6.59 %) > Cu (5.11 %) > Ni (1.47 %) > Cr (1.17 %) > Mn (0.84 %) > Cd (0.76 %) > Zn (0.50 %) > Pb (0.31 %). The two main sources of heavy metals included tire and mechanical abrasion (24.5 %) and traffic exhaust (21.6 %). All dust extracts induced cytotoxicity, evidenced by stronger inhibition of cell viability, higher production of ROS, and altered mRNA expression of antioxidant enzymes and cell cycle-related genes, with commercial- areas-2 (CA2)-dust extract showing the greatest oxidative damage and cell cycle arrest. Our data may provide new evidence that dust exposure in high geological background cities could trigger human cornea damage.

Pollution and mobility of heavy metals in the soils of a typical agricultural zone in eastern China

The pollution of heavy metals (HMs) in agricultural soils profoundly threatens national food safety, and the mobility and environmental behaviors of HMs are closely implicated in crop safety. Here, we assessed the pollution level and mobility of ten HMs and explored their environmental behaviors in the soils of three different land uses from a main crop production zone in eastern China. The concentrations of HMs in the soils were higher in the farmland than the woodland and wasteland, and Cd showed a relatively higher pollution and ecological risk levels compared to other metals. Cadmium was dominated by the reducible (41%) and exchangeable (23%) fractions, and the rest of HMs were mainly in the residual fraction (> 60%). The significant correlation between the exchangeable and DGT-labile Cd indicates relatively higher mobility of Cd in the soils. Soil pH, organic matters and mineral elements had significant correlation with the exchangeable and reducible fractions of most of the HMs (e.g., Cd, Co, Mn, Ni, Pb and V; p < 0.05), indicating their good predictors of the HMs mobility. However, this was not the case for the DGT-labile fraction, which suggests a marked difference in the controlling mechanisms of the mobility versus potential bioavailability of HMs in the soils. The results of this study indicate that both the chemically extracted fractions and the bioavailable fractions of HMs need be considered when effectively assessing the safety of agricultural soils.

The main strategies for soil pollution apportionment: A review of the numerical methods

Nowadays, a large number of compounds with different physical and chemical properties have been determined in soil. Environmental behaviors and source identification of pollutants in soil are the foundation of soil pollution control. Identification and quantitative analysis of potential pollution sources are the prerequisites for its prevention and control. Many efforts have made to develop methods for identifying the sources of soil pollutants. These efforts have involved the measurement of source and receptor parameters and the analysis of their relationships via numerical statistics methods. We have comprehensively reviewed the progress made in the development of source apportionment methodologies to date and present our synthesis. The numerical methods, such as spatial geostatistics analysis, receptor models, and machine learning methods are addressed in depth. In most cases, however, the effectiveness of any single approach for source apportionment remains limited. Combining multiple methods to address soil quality problems can reduce uncertainty about the sources of soil pollution. This review also constructively highlights the key strategies of combining mathematical models with the assessment of chemical profiles to provide more accurate source attribution. This review intends to provide a comprehensive summary of source apportionment methodologies to help promote further development.

Health risk assessment of heavy metals in wild fish and seasonal variation and source identification of heavy metals in sediments: a case study of typical urban river in Xi'an, China

In order to determine the status of heavy metal pollution in river sediments and wild fish in Xi'an, concentrations of heavy metals (Cr, Ni, Cu, Zn, As, and Pb) were collected and analyzed in sediments and wild fish during dry season (October-November 2020) and wet season (June-July 2021). This study aimed to investigate the spatial and temporal variations of heavy metals in urban rivers of Xi'an, China. Their distribution characteristics and sources as well their pollution levels and health risks were assessed. The findings revealed that influenced by human activities, the heavy metal content in sediments (mg·kg-1 dry weight) in wet season was ranked as follows: Cr (73.09) > Zn (63.73) > Pb (40.31) > Ni (31.52) > Cu (24.86) > As (6.83); in the dry season: Zn (94.07) > Cr (69.59) > Cu (34.24) > Ni (33.60) > Pb (32.87) > As (7.60). Moreover, 32 fish samples from six species indicated an average metal content trend (mg·kg-1 wet weight) of Zn (8.70) > Cr (0.57) > Pb (0.28) > Ni (0.27) > Cu (0.24) > As (0.05). The potential ecological risk indices for sediment heavy metal concentrations in both seasons were well below the thresholds, which indicates that the aquatic environment is in safe level. The analysis of the potential ecological risk of sediment heavy metal concentrations indicates that the aquatic environment is safe for the time being. Based on the estimated daily intake (EDI), target risk quotient (THQ), total target risk quotient (TTHQ), cancer risk (CR), total cancer risk (TCR), and the permissible safety limits set by the agencies, the consumption of the fish examined is safe for human health. However, the presence of Cr and As in wild fish should still be a concern for human health, especially for children. The cumulative effect of heavy metals and the bioconcentration factor (BCF) suggest that sediment and heavy metals in fish are closely related, with higher concentrations in fish living in the bottom layer of the water column than in other water layers, and increasing with increasing predator levels. Correlation analysis and PMF modeling identified and determined four comparable categories of potential sources, namely, (1) atmospheric deposition and traffic sources, (2) agricultural sources, (3) industrial sources, and (4) natural sources.

Bioavailability and health risk of pollutants around a controlled landfill in Morocco: Synergistic effects of landfilling and intensive agriculture

Toxic contamination of agricultural soils by trace metal(oid)s can pose detrimental effects on human health and agroecological systems. In this view, the current research explored total and available metal(oid)s in surface soils and assessed the associated hazards using pollution indices, PMF modeling, PCA, and Montecarlo probabilistic human risk assessment with 10,000 repetitions. The mean concentrations of Cd, Pb, As, Cr, Ni, Cu, Zn, and Fe were 0.89, 24.86, 1.81, 19.10, 25.44, 7.98, 49.12 and 6183.32 mg kg-1 dry weight, respectively. These findings highlighted that the concentration of pollutants exceeded the values measured in the geochemical background. Soil enrichment by heavy metal (oid)s was confirmed by analyzing available fractions using DTPA ,CaCl2 and enrichment factor (EF). Additionally, pollution indicators (Igeo, PLI, and PERI) displayed significant contamination levels, with a higher ecological risk. Matrix Factorization (PMF) receptor and multivariate statistical analysis reflected that anthropogenic activities, particularly landfilling and agricultural practices were the main causes of the contamination. Furthermore, probabilistic and deterministic human risk assessments showed that carcinogenic risks exceeded the threshold values (10-4) set by the USEPA. Consequently, it is crucial to implement continuous monitoring and supervision of landfill sites to prevent additional pollution. These measures should be integrated into the management plans for waste management.

Accumulation of livestock manure-derived heavy metals in the Hexi Corridor oasis agricultural alkaline soil and bioavailability to Chinese cabbage (Brassica pekinensis L.) after 4-year continuous application

Hexi Corridor is one of the most important base of vegetable producing areas in China. Livestock manure (LM) applied to agricultural field could lead to soil heavy metal (HM) pollution. Previous studies have focused on HM pollution following LM application in acidic polluted soils; however, fewer studies have been conducted in alkaline unpolluted soils. A 4-year field vegetable production experiment was conducted using pig manure (PM) and chicken manure (CM) at five application rates (0, 15, 30, 45, and 60 t ha-1) to elucidate potential risks of HMs in an alkaline unpolluted soil in the Hexi Corridor oasis agricultural area and HM uptake by Chinese cabbage. The results showed that LM application caused a significant build-up of Cu, Zn, Pb, Cd, and Ni content in topsoil by 30.6-99.7%, 11.4-51.7%, 1.4-31.3%, 5.6-44.9%, 14%-40.8%, respectively. The Cd, Cu, Zn could potentially exceed the soil threshold in next 8-65 years after 15-60 t ha-1 LM application. Under LM treatment, the soil DTPA-extractable Cu, Zn, Fe, the acid-extractable fraction of Cu, Zn, Fe, Cd, Ni, and the Oxidable fraction of Cu, Zn, Fe, Mn, Cd, Ni significantly increased, but the DTPA-extractable Pb, Cd, the acid-extractable fraction of Pb, and the reducible fraction of Cd significantly decreased. Cu and Zn could migrate to the deeper soil and relatively increase in DTPA-extracted Cu, Zn were found in 20-40 cm soil depth after LM application. The pH and SOM could influence the bioavailability of HMs in soil. The bioaccumulation factor and transfer factor (TF) values were <1 except Mn (TF > 1). HMs in leaf did not approach the threshold for HM toxicity due to the "dilution effect". Recommend the type of manure was the PM and the annual PM application rate was 30 t ha-1 to ensure a 20-year period of clean production in alkaline unpolluted Fluvo-aqiuc vegetable soils.

Assessment and machine learning prediction of heavy metals fate in mining farmland assisted by Positive Matrix Factorization

The accurate pollutant prediction by Machine Learning (ML) is significant to efficient environmental monitoring and risk assessment. However, application of ML in soil is under studied. In this study, a Positive Matrix Factorization (PMF) assisted prediction method was developed with Support Vector Machine (SVM) and Random Forest (RF) for heavy metals (HMs) prediction in mining farmland. Principal Component Analysis (PCA) and Redundancy Analysis (RDA) were selected to pretreat data. Experiment results illustrated Cd was the main pollutant with heavy risks in the study area and Pb was easy to migrate. The method effects of HMs total concentration predicting were PMF > Simple > PCA > PCA - PMF, and RF predicted better than SVM. Data pretreatment by RDA prior inspection improved the model results. Characteristic HMs Tessier fractions prediction received good effects with average R value as 0.86. Risk classification prediction performed good in Cd, Cu, Ni and Zn, however, Pb showed weak effect by simple model. The best classifier method for Pb was PMF - RF method with relatively good effect (Area under ROC Curve = 0.896). Overall, our study suggested the combination between PMF and ML can assist the prediction of HMs in soil. Spatial weighted attribute of HMs can be provided by PMF.

Source-sink response analysis of heavy metals and soil pollution assessment in non-ferrous metal industrial agglomeration areas based on decision unit

Soil heavy metals (HMs) pollution is a worldwide concern. In this study, decision units based on "source-sink relationship" were established using multi-source data. The source-sink response of heavy metals in agricultural soils at the regional scale was analyzed using machine learning methods, receptor models, and geospatial analysis. The comprehensive pollution risk score (CRS) was proposed by integrating a variety of key evaluation indicators to evaluate the pollution degree of each decision unit. We divided the study area into 193 decision units, the proportions of sites with concentrations of Cd, Hg, As, Pb, and Cr exceeding the most stringent risk screening values were 16.4 %, 2.2 %, 4.0 %, 7.6 %, and 0.2 %, respectively. Agricultural activities (livestock manure, fertilizer, sewage irrigation), industrial activities (rare earth ore and tungsten‑molybdenum mining and smelting), and soil parent material are the dominant pollution sources of HMs in the study area. The risk of contamination of each element is ranked from largest to smallest according to the CRS as Cd > Hg > Pb > As > Cr. The western and southwestern water pollution decision units are the areas with the highest risk of soil HMs contamination. This quantitative evaluation framework can provide a relatively accurate decision basis for soil pollution management.

Microplastic particles in river sediments and water of southwestern Nigeria: insights on the occurrence, seasonal distribution, composition, and source apportionment

Microplastics (MPs) are globally recognized as an emerging environmental threat, particularly in the aquatic environment. This study presents baseline data on the occurrence and distribution of MPs in sediments and surface water of major rivers in southwestern Nigeria. Microplastics were extracted by density separation and polymer identification using Fourier transformed infrared spectroscopy in attenuated total reflectance mode (FTIR-ATR). The abundance of MPs in surface sediment and water samples across all locations ranged from 12.82 to 22.90 particle/kg dw and 6.71 to 17.12 particle/L during the dry season and 5.69 to 14.38 particle/kg dw and 12.41 to 22.73 particle/L during the wet season, respectively. On average, fiber constituted the highest percentage of MP in sediments (71%) and water (67%) while foam accounted for the lowest values of 0.6% and 1.7%, respectively. Polypropylene (PP) and polyethylene (PE) were the main MPs across all locations based on Fourier transform infrared spectroscopy (FTIR). MPs of size < 1 mm were the most abundant (≥ 55%) on average in both water and sediments. The study identified run-off from human activities and industrial wastewater as potential sources of MP exposure based on positive matrix factorization. The study suggests assessing the impact of different land-use activities on MPs occurrence and distribution in addition to quantifying MPs in seafood as a way forward in food safety management systems for further studies. This study confirmed the occurrence and widespread distribution of MPs in surface water and sediments and provides a database on MP pollution in Nigeria.

Source-based health risk assessment of heavy metal contamination in soil: a case study from a polymetallic mining region in Southeastern Hubei, Central China

To conduct a precise health risk assessment of heavy metals (HMs) in soil, it is imperative to ascertain the primary sources of potential health risks. In this study, we conducted comprehensive measurements of HMs, specifically focusing on the accumulation of Cu, Cd, Sb, Zn, and Pb in local soil, which may pose threats to environmental quality. To achieve our objective, we employed a method that combines positive matrix factorization with a health risk assessment model to quantify the health risks associated with specific sources. The results obtained from the geo-accumulation index indicate that the majority of HMs found in the local soil are influenced by anthropogenic activities. Among these sources, local industrial-related activities contributed the largest proportion of HMs to the soil at 34.7%, followed by natural sources at 28.7%, mining and metallurgy-related activities at 28.2%, and traffic-related activities at 8.40%. Although the non-carcinogenic and carcinogenic risks associated with individual HMs were found to be below safety thresholds, the cumulative health risks stemming from total HMs exceeded safety limits for children. Moreover, the unacceptable health risks for children originating from industrial-related activities, natural sources, and mining and metallurgy-related activities were primarily concentrated in proximity to mining sites and industrial areas within the local region. This investigation furnishes valuable insights that can aid governmental authorities in formulating precise control policies to mitigate health threats posed by soils in polymetallic mining areas.